Automated White Space Identification via Taxonomy Analysis
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Solution Overview
Problem
Current methods for identifying white space opportunities in patent and web-related analytics are time-consuming and ineffective, requiring multiple labor-intensive sessions with manual processing of search results that may not yield valuable outputs.
Innovation Solution
A method that uses keywords to construct snippets from textual data, categorize them, create mathematical models, and analyze documents to identify white spaces by incorporating domain expertise and generating taxonomies based on feature spaces, allowing for the automatic classification and identification of potential white space opportunities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual search and processing methods are used to identify white space opportunities, then the process can be performed with simple tools, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The patent replaces manual mechanical search and analysis processes with automated computational systems. The system automatically retrieves patent documents, extracts features, creates taxonomies, and identifies white space opportunities without requiring manual search sessions, thereby dramatically improving productivity while reducing time loss.
Solution Approach 2:
The system performs self-service by automatically conducting the entire white space analysis process including document retrieval, feature extraction, taxonomy creation, and gap identification. The automated system serves itself to complete tasks that previously required human researchers to conduct multiple manual search sessions.
2Reliability
If comprehensive patent analysis is performed to ensure accurate white space identification, then the reliability of results improves, but the device complexity increases
Solution Approach 1:
The patent segments the complex analysis process into distinct modular components: document retrieval, feature extraction, taxonomy creation, and white space identification. Each module performs a specific function independently, which maintains high reliability through specialized processing while managing overall system complexity through modular design.
Solution Approach 2:
The patent introduces intermediary structures such as taxonomies and feature spaces that mediate between raw patent data and white space identification. These intermediaries organize and structure the data in systematic ways, improving reliability by ensuring comprehensive analysis while managing complexity through structured intermediate representations.
3Productivity
If automated classification systems are implemented to reduce manual processing, then productivity increases, but the difficulty of detecting and measuring white space accurately may worsen
Solution Approach 1:
The patent performs preliminary actions by automatically retrieving patent documents and extracting features before classification. This preliminary processing ensures that all relevant data is captured and structured beforehand, enabling the automated classification system to maintain high productivity while preserving detection accuracy through comprehensive pre-processing.
Solution Approach 2:
The system incorporates feedback mechanisms where classification results inform subsequent analysis steps. The automated system uses feedback from feature extraction and initial classification to refine taxonomy creation and improve white space detection accuracy, ensuring that productivity gains do not compromise measurement precision.
Data Source
AI summary
A method for analyzing predefined subject matter in a patent database being for use with a set of target patents, each target patent related to the predefined subject matter, the method comprising: creating a feature space based on frequently occurring terms found in the set of target patents; creating a partition taxonomy based on a clustered configuration of the feature space; editing the partition taxonomy using domain expertise to produce an edited partition taxonomy; creating a classification taxonomy based on structured features present in the edited partition taxonomy; creating a contingency table by comparing the edited partition taxonomy and the classification taxonomy to provide entries in the contingency table; and identifying all significant relationships in the contingency table to help determine the presence of any white space.